---
license: apache-2.0
tags:
  - object-detection
  - chart-understanding
  - document-ai
  - cascade-rcnn
  - swin-transformer
---

# Chart Element Detector

A chart element detection model based on
[CACHED](https://github.com/pengyu965/ChartDete)
(Context-Aware Chart Element Detection).

## Model Details

- **Architecture**: Cascade R-CNN + Swin Transformer + FPN
- **Task**: Chart element detection and localization
- **Classes**: 18 chart element classes
- **Dataset**: PMC Chart Dataset
- **COCO AP**: 0.729

## Classes

x_tick_label, y_tick_label, x_tick, y_tick,
x_axis_title, y_axis_title, chart_title,
legend_marker, legend_label, legend_title,
value_label, mark_label, tick_grouping,
plot_area, x_axis_area, y_axis_area,
legend_area, others

## Output Format
```json
{
  "chart": [
    {
      "x1": 10.0,
      "y1": 20.0,
      "x2": 100.0,
      "y2": 200.0,
      "score": 0.95,
      "class": "chart_title"
    }
  ]
}
```

## Requirements
```
torch==1.13.1
mmdet==2.28.2
mmcv-full==1.7.0
```

## Citation
```bibtex
@inproceedings{yan2023cached,
  title={CACHED: Context-Aware Chart Element Detection},
  author={Yan, Pengyu and Ahmed, Saleem and Doermann, David},
  booktitle={ICDAR},
  year={2023}
}
```
```